A practical tutorial for recording model tool requests, real function results, patches, checks, screenshots, and a saved run log. The post How to Debug AI Coding Agents When They Change the Wrong Thing appeared first on Towards Data Science .
The article explains how the five core assumptions of MLOps monitoring are violated when agents are deployed to production, leading to inherited signals that incorrectly mark failed runs as healthy. It highlights the specific ways in which agent-based systems disrupt traditional monitoring stacks and the implications for reliability and performance. The piece serves as a warning for practitioners transitioning from MLOps to AgentOps, outlining the critical monitoring gaps that arise.
The article discusses the essential skill of effectively instructing coding agents and verifying their changes. It highlights that while line‑by‑line code review is one method, it is not the most efficient way to validate software changes. The focus is on confidently guiding agents and confirming correct implementation without exhaustive inspection.
The article titled "What's in the Box? Distinguishing Acceptable Traces from Unacceptable Ones" discusses how to transform scattered requirements into deployable agents. It focuses on identifying which traces are acceptable and which are not for effective deployment. The piece was originally published on Towards Data Science.
The article describes an experiment where an AI agent was able to identify incorrect model decisions while interacting with a web page, yet failed to correct the errors. By replaying correct decisions through four intentionally flawed browser actions, the author demonstrates that a simple page check missed half of the erroneous clicks. This illustrates that detecting a fault and successfully recovering from it are distinct capabilities.
The article discusses how an agent can continue to produce confident answers even when its tools fail, leading to a misleadingly positive dashboard display. It highlights the issue of agents generating responses from nothing after tool breakdowns, which can create a false sense of reliability. The piece serves as a cautionary note for developers deploying agentic AI systems.
The article describes a tool called CodeOwnership that measures how much of the code an AI writes can be attributed to the user. The author shares a personal experiment where they rated their confidence at 4 out of 5 and then spent 8 minutes trying to locate a planted bug, ultimately finding none. They provide a walkthrough of the tool’s usage, its results, and discuss its limitations.
Simon Willison reports that Calif Research has released a demo of WeWorm, a zero‑click worm that spreads via WeChat calls on iOS and Android. The worm requires no user interaction; even if a call is answered, nothing is heard, yet the exploit still succeeds. Using AI, the team identified the bug, wrote a remote code execution exploit in about two days, and built the worm in an additional week, a process that traditionally would have taken a larger team months.
The article describes an experiment where the author intentionally broke a browser agent by replaying correct model decisions through four faulty browser actions. A basic page check failed to detect half of the incorrect clicks and incorrectly flagged every successful click within an embedded page. The piece highlights the challenges of diagnosing and resolving such browser-related faults.
The article titled "Your Agent Works in the Demo. Now Make It Survive Monday" discusses the transition from a simple demonstration to a more robust, real‑world application. It highlights the challenges of ensuring an agent remains functional beyond a controlled demo environment. The piece appears on Towards Data Science.
The article titled "How to Maximize Your Coding Agent Subscriptions" discusses ways to get more out of your coding agent subscriptions. It was published on Towards Data Science and focuses on optimizing the use of these tools. The post offers practical tips and strategies for users to enhance their experience with coding agents.
The article explains how to detect a payload that appears correct yet is not, by employing a watchdog pattern in Python. It discusses the challenges that cause many multi‑agent systems to fail even when their evaluations succeed. The post was originally published on Towards Data Science.